U.S. patent application number 10/917985 was filed with the patent office on 2005-05-19 for video surveillance system with rule-based reasoning and multiple-hypothesis scoring.
Invention is credited to Gong, Yihong, Han, Mei, Tao, Hai.
Application Number | 20050104962 10/917985 |
Document ID | / |
Family ID | 34576979 |
Filed Date | 2005-05-19 |
United States Patent
Application |
20050104962 |
Kind Code |
A1 |
Han, Mei ; et al. |
May 19, 2005 |
Video surveillance system with rule-based reasoning and
multiple-hypothesis scoring
Abstract
A video surveillance system uses rule-based reasoning and
multiple-hypothesis scoring to detect predefined behaviors based on
movement through zone patterns. Trajectory hypothesis spawning
allows for trajectory splitting and/or merging and includes local
pruning to managed hypothesis growth. Hypotheses are scored based
on a number of criteria, illustratively including at least one
non-spatial parameter. Connection probabilities computed during the
hypothesis spawning process are based on a number of criteria,
illustratively including object size. Object detection and
probability scoring is illustratively based on object class.
Inventors: |
Han, Mei; (Cupertino,
CA) ; Gong, Yihong; (Cupertino, CA) ; Tao,
Hai; (Santa Cruz, CA) |
Correspondence
Address: |
Ronald D. Slusky
Suite 5L
353 West 56th Street
New York
NY
10019-3775
US
|
Family ID: |
34576979 |
Appl. No.: |
10/917985 |
Filed: |
August 12, 2004 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
60520610 |
Nov 17, 2003 |
|
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|
Current U.S.
Class: |
348/143 ;
382/103 |
Current CPC
Class: |
G06K 9/00335 20130101;
G06K 9/00771 20130101; G08B 13/19608 20130101 |
Class at
Publication: |
348/143 ;
382/103 |
International
Class: |
G06K 009/00; H04N
007/18; H04N 009/47 |
Claims
1. A method for use in a video surveillance system, the method
comprising generating a plurality of hypotheses, each hypothesis
comprising a respective different set of hypothesized trajectories
of objects hypothesized to have been moving through an area under
surveillance at a particular time, identifying at least one of said
hypotheses as being more likely than others of said hypotheses to
represent the actual trajectories of the actual objects moving
through the area under surveillance at said particular time, and
utilizing said at least said one of said hypotheses to determine
whether at least one predefined alert condition has occurred within
the area under surveillance.
2. The method of claim 1 wherein said identifying includes
computing a likelihood for each of said hypotheses, said likelihood
being a function of connection probabilities each associated with a
respective trajectory of that hypothesis, each connection
probability being a measure of the probability that an object that
terminates said associated trajectory is, in fact, an object in the
area under surveillance that is on that trajectory.
3. The method of claim 2 wherein said at least one of said
hypotheses is the one having the largest computed likelihood.
4. The method of claim 1 wherein each of said hypotheses is
generated by detecting particular objects in the area under
surveillance at said particular time, and extending a previously
generated hypothesis that comprises a particular set of
hypothesized trajectories of objects hypothesized to have been
moving through the area under surveillance at a previous time, said
extended hypothesis comprising 1) at least ones of the trajectories
of the previously generated hypothesis, 2) one or more of the
detected objects, and 3) a particular set of connections extending
at least ones of the trajectories of the previously generated
hypothesis to at least ones of the detected objects.
5. The method of claim 1 wherein said objects are people and
wherein said alert condition includes a predetermined pattern of
movement of one or more people relative to at least one of a) at
least one other person, b) one or more fixed objects, or c) one or
more predefined zones within said area under surveillance.
6. The method of claim 5 wherein said predetermined pattern of
movement includes at least one time criterion.
7. A method for use in an electronic surveillance system, the
method comprising establishing a plurality of hypotheses associated
with a particular point in time, each hypothesis comprising a set
of objects hypothesized to be in an area under surveillance by said
system at said particular point in time and further comprising
hypothesized prior trajectories through said area under
surveillance for at least ones of the hypothesized objects,
detecting objects present in the area under surveillance at a
subsequent point in time, establishing a plurality of extended
hypotheses associated with said subsequent point in time, each
extended hypothesis being associated with one of the previously
established hypotheses and comprising 1) at least ones of the
trajectories of the associated previously established hypothesis,
2) one or more of the detected objects, and 3) a particular set of
connections extending at least ones of the trajectories to said one
or more of the detected objects, each extended hypothesis thereby
comprising a set of objects hypothesized to be in the area under
surveillance at the subsequent point in time and further comprising
hypothesized prior trajectories through said area under
surveillance for at least ones of said set of hypothesized objects,
repeating said detecting and said establishing extended hypotheses
multiple times, identifying at least one of the hypotheses
associated with said subsequent point in time as being more likely
than others of the hypotheses associated with that point in time as
comprising the actual trajectories of the actual objects in the
area under surveillance at that point in time, and utilizing the
identified hypothesis to determine whether at least one predefined
alert condition has occurred within the area under
surveillance.
8. The method of claim 7 wherein said identifying includes
computing a likelihood for each of said hypotheses, said likelihood
being a function of connection probabilities each associated with a
respective trajectory of that hypothesis, each connection
probability being a measure of the probability that an object that
terminates said associated trajectory is, in fact, an object in the
area under surveillance that is on that trajectory.
9. The method of claim 8 wherein said identified hypothesis is the
one having the largest computed likelihood.
10. The method of claim 7 wherein said objects are people and
wherein said alert condition includes a predetermined pattern of
movement of at least one person relative to a fixed object in said
area under surveillance.
11. The method of claim 7 wherein said objects are people and
wherein said alert condition includes a predetermined pattern of
movement that includes at least one time criterion.
12. The method of claim 7 wherein said objects are people and
wherein said alert condition includes a predetermined pattern of
movement between two or more people.
13. The method of claim 12 wherein said alert condition further
includes a predetermined pattern of movement between at least one
of said people and a fixed object in said area under
surveillance.
14. The method of claim 7 wherein said objects are people and
wherein said alert condition includes a predetermined pattern of
movement of one or more people relative to at least one of a) at
least one other person, b) one or more fixed objects, or c) one or
more predefined zones within said area under surveillance.
15. The method of claim 14 wherein said predetermined pattern
includes at least one time criterion.
16. An electronic surveillance system adapted to carry out the
method defined by claim 1.
17. An electronic surveillance system adapted to carry out the
method defined by claim 7.
18. A tangible medium on which are stored instructions that are
executable by a processor to carry out the method defined by claim
1.
19. A tangible medium on which are stored instructions that are
executable by a processor to carry out the method defined by claim
7.
Description
CROSS-REFERENCE TO RELATED APPLICATION
[0001] This application claims the benefit of U.S. provisional
patent application No. 60/520,610 filed Nov. 17, 2003, incorporated
herein by reference.
BACKGROUND OF THE INVENTION
[0002] Multiple object tracking has been one of the most
challenging research topics in computer vision. Indeed, accurate
multiple object tracking is the key element of video surveillance
system where object counting and identification are the basis of
determining when security violations within the area under
surveillance are occurring.
[0003] Among the challenges in achieving accurate tracking in such
systems are a number of phenomena. These phenomena include a) false
detections, meaning that the system erroneously reports the
presence of an object, e.g., a human being, at a particular
location within the area under surveillance a particular time; b)
missing data, meaning that the system has failed to detect the
presence of an object in the area under surveillance that actually
is there; c) occlusions, meaning that an object being tracked has
"disappeared" behind another object being tracked or some fixed
feature (e.g., column or partition) within the area under
surveillance; d) irregular object motions, meaning, for example,
that an object that was moving on a smooth trajectory has abruptly
stopped or changed direction; e) changing appearances of the
objects being tracked due, for example, to changed lighting
conditions and/or the object presenting a different profile to the
tracking camera.
[0004] Among the problems of determining when security violations
have occurred or are occurring is the unavailability of electronic
signals that could be profitably used in conjunction with the
tracking algorithms. Such signals include, for example, signals
generated when a door is opened or when an access device, such as a
card reader, has been operated. Certainly an integrated system
built "from the ground up" could easily be designed to incorporate
such signaling, but it may not be practical or economically
justifiable to provide such signals to the tracking system when the
latter is added to a facility after the fact.
SUMMARY OF THE INVENTION
[0005] The present invention addresses one or more of the above
problems, as well as possibly addressing other problems as well.
The invention is particularly useful when implemented in a system
that incorporates the inventions that are the subject of the
co-pending United States patent applications listed at the end of
this specification.
[0006] A video surveillance system embodying the principles of the
invention generates a plurality of hypotheses, each of which
comprises a respective different set of hypothesized trajectories
of objects hypothesized to have been moving through an area under
surveillance at a particular time. At least a particular one of the
hypotheses is identified as being more likely than others of the
hypotheses to represent the actual trajectories of the actual
objects moving through the area under surveillance at that
particular time. That particular one of the hypotheses is used to
determine whether at least one predefined alert condition has
occurred within the area under surveillance.
[0007] In particular embodiments of the invention, a likelihood is
computed for each of the hypotheses and the hypothesis having the
largest computed likelihood is the one used to determined whether
an alert condition has occurred. The computed likelihood is a
function of connection probabilities each associated with a
respective trajectory of the hypothesis in question. Each
connection probability is a measure of the probability that an
object that terminates the associated trajectory is, in fact, an
object in the area under surveillance that is on that
trajectory.
[0008] In particular embodiments of the invention, the objects that
are tracked are people and the at least one alert condition
includes a predetermined pattern of movement of one or more people
relative to at least one of a) at least one other person, b) one or
more fixed objects, or c) one or more predefined zones within the
area under surveillance. The predetermined pattern of movement may
include at least one time criterion.
[0009] In particular embodiments of the invention, the hypotheses
for a particular point in time are generated by extending
hypotheses generated for a previous point in time.
DRAWING
[0010] FIG. 1A is a block diagram of image-based multiple-object
tracking system embodying the principles of the invention;
[0011] FIG. 1B illustrates the operation of an alert reasoning
portion of the system of FIG. 1A;
[0012] FIG. 1C is a flow diagram illustrating the operation of the
alert reasoning portion of the system in detecting the occurrence
of the alert conditions referred to as tailgating and
piggy-backing;
[0013] FIGS. 2A through 2F depict various patterns of movement that
are indicative of alarm conditions of a type that the system of
FIG. 1A is able to detect;
[0014] FIGS. 3A and 3B depict two possible so-called hypotheses,
each representing a particular unique interpretation of object
detection data generated by the system of FIG. 1A over a period of
time.
[0015] FIG. 4 is a generalized picture illustrating the process by
which each of the hypotheses generated for a particular video frame
can spawn multiple hypotheses and how the total number of
hypotheses is kept to manageable levels
[0016] FIG. 5, shows a process carried out by the hypothesis
generation portion of the system of FIG. 1A in order to implement
so-called local pruning;
[0017] FIG. 6 indicates how data developed during the processing
carried out in FIG. 5 is used to spawn new hypotheses;
[0018] FIG. 7 shows a simplified example of how hypotheses are
generated;
[0019] FIG. 8 shows the processing carried out within the
hypothesis management portion of the system of FIG. 1A; and
[0020] FIGS. 9A through 9E graphically depict the process by which
the system of FIG. 1A detects the presence of humans in the area
under surveillance.
DETAILED DESCRIPTION
[0021] The image-based multiple-object tracking system in FIG. 1A
is capable of tracking various kinds of objects as they move, or
are moved, through an area under surveillance. Such objects may
include, for example, people, animals, vehicles or baggage. In the
present embodiment, the system is arranged to track the movement of
people. Thus in the description that follows terms including
"object," "person," "individual," "human" and "human object" are
used interchangeably except in instances where the context would
clearly indicate otherwise.
[0022] The system comprises three basic elements: video camera
1502, image processing 103 and alert reasoning 132.
[0023] Video camera 102 is preferably a fixed or static camera that
monitors and provides images as sequences of video frames. The area
under surveillance is illustratively secure area 23 shown in FIG.
2A. A secured access door 21 provides access to authorized
individuals from a non-secure area 22 into secure area 23.
Individuals within area 22 are able to obtain access into area 23
by swiping an access card at an exterior access card reader 24
located near door 21 in non-secure area 22, thereby unlocking
and/or opening door 21. In addition, an individual already within
area 22 seeking to leave that area through door 21 does so by
swiping his/her access card at an interior access card reader 25
located near door 21 in secure area 23.
[0024] Image processing 103 is software that processes the output
of camera 102 and generates a so-called "top hypothesis" 130. This
is a data structure that indicates the results of the system's
analysis of some number of video frames over a previous period of
time up to a present moment. The data in top hypothesis 130
represents the system's assessment as to a) the locations of
objects most likely to actually presently be in the area under
surveillance, and b) the most likely trajectories, or tracks, that
the detected objects followed over the aforementioned period of
time.
[0025] An example of one such hypothesis is shown in FIG. 3A. In
this FIG., the nodes (small circles) represent object detections
and the lines connecting the nodes represent the movement of
objects between frames. This hypothesis is associated with the most
recent video frame, identified by the frame number i+4. As seen in
the rightmost portion of the FIG., four objects labeled Q, R, S and
T were detected in frame i+4 and it has been determined--by having
tracked those objects through previous frames, including frames i,
i+1, i+2 and i+3--that those objects followed the particular
trajectories shown. The frame index i illustratively advances at a
rate of 10 frames/second, which provides a good balance between
computational efficiency and tracking accuracy. FIG. 3A is
described in further detail below.
[0026] Referring again to FIG. 1A, top hypothesis 130 is applied to
alert reasoning 132. This is software that analyzes top hypothesis
130 with a view toward automatically identifying certain abnormal
behaviors, or "alert conditions," that the system is responsible to
identify based on predefined rules. It is, of course, possible to
set up sensors to detect the opening and closing of a door.
However, the image-based system disclosed herein provides a way of
confirming if objects, e.g., people, have actually come through the
door and, if so, how many and over what period of time.
[0027] The system utilizes a number of inventions to a) analyze the
video data, b) determine the top hypothesis at any point in time
and b) carry out the alert reasoning.
Alert Reasoning
[0028] The alert conditions are detected by observing the movement
of objects, illustratively people, through predefined areas of the
space under surveillance and identifying an alert condition as
having occurred when area-related patterns are detected. A
area-related pattern means a particular pattern of movement through
particular areas, possibly in conjunction with certain other
events, such as card swiping and door openings/closings. Thus
certain of the alert conditions are identified as having occurred
when, in addition to particular movements through the particular
areas having occurred, one or more particular events also occur. If
door opening/closing or card swiping information is not available,
alert reasoning 132 is nonetheless able to identify at least
certain alert conditions based on image analysis alone, i.e., by
analyzing the top hypothesis.
[0029] As noted above, the objects tracked by this system are
illustratively human beings and typical alert conditions are those
human behaviors known as tailgating and piggy-backing. Tailgating
occurs when one person swipes an access control card or uses a key
or other access device that unlocks and/or opens a door and then
two or more people enter the secure area before the door is
returned to the closed and locked position. Thus, in the example of
FIG. 2A, tailgating occurs if more than one person enters secure
area 23 with only one card swipe having been made. This implies
that after the card was swiped and door 21 was opened, two people
passed through the door before it closed. Piggy-backing occurs when
a person inside the secure area uses an access control card to open
the door and let another person in. Thus in the example of FIG. 2A,
piggy-backing occurs if a person inside secure area 23 swipes
his/her card at reader 25 but instead of passing through door 21
into non-secure area 22 allows a different person, who is then in
non-secure area 22, to pass through into secure area 23. An
illustrative list of behaviors that the system can detect, in
addition to the two just described, appears below.
[0030] Alert reasoning module 132 generates an alert code 134 if
any of the predefined alert conditions appear to have occurred. In
particular, based on the information in the top hypothesis, alert
reasoning module 132 is able to analyze the behaviors of the
objects--which are characterized by object counts, interactions,
motion and timing--and thereby detect abnormal behaviors,
particularly at sensitive zones, such as near the door zone or near
the card reader(s). An alert code, which can, for example, include
an audible alert generated by the computer on which the system
software runs, can then be acted upon by an operator by, for
example, reviewing a video recording of the area under surveillance
to confirm whether tailgating, piggy-backing or some other alert
condition actually did occur.
[0031] Moreover, since objects can be tracked on a continuous
basis, alert reasoning module 132 can also provide traffic reports,
including how many objects pass through the door in either
direction or loiter at sensitive zones.
[0032] The analysis of the top hypothesis for purposes of
identifying alert conditions may be more fully understood with
reference to FIGS. 2A through 2F, which show the area under
surveillance--secure area 23--divided into zones. There are
illustratively three zones. Zone 231 is a door zone in the vicinity
of door 21. Zone 232 is a swipe zone surrounding zone 231 and
includes interior card reader 25. Door zone 231 and swipe zone 232
may overlap to some extent. Zone 233 is an appearing zone in which
the images of people being tracked first appear and from which they
disappear. The outer boundary of zone 233 is the outer boundary of
the video image captured by camera 102.
[0033] Dividing the area under surveillance into zones enables the
system to identify alert conditions. As noted above, an alert
condition is characterized by the occurrence of a combination of
particular events. One type of event is appearance of a person in a
given zone, such as the sudden appearance of a person in the door
zone 231. Another type of event is the movement of a person in a
given direction, such as the movement of the person who appeared in
door zone 231 through swipe zone 232 to the appearing zone 233.
This set of facts implies that someone has come into secure area 23
through door 21. Another type of event is an interaction, such as
if the trajectories of two objects come from the door together and
then split later. Another type of event is a behavior, such as when
an object being tracked enters the swipe zone. Yet another type of
event relates to the manipulation of the environment, such as
someone swiping a card though one of card readers 24 and 25. The
timing of events is also relevant to alert conditions, such as how
long an object stays at the swipe zone and the time difference
between two objects going through the door.
[0034] Certain movement patterns represent normal,
non-security-violative activities. For example, FIG. 2B shows a
normal entrance trajectory 216 in which a person suddenly appears
in door zone 231 and passes through swipe zone 232 and appearance
zone 233 without any other trajectory being detected. As long as
the inception of this trajectory occurred within a short time
interval after a card swipe at card reader 24, the system
interprets this movement pattern as a normal entrance by an
authorized person. A similar movement pattern in the opposite
direction is shown in FIG. 2C, this representing a normal exit
trajectory 218.
[0035] FIGS. 2D-2F illustrate patterns of multiple trajectories
through particular zones that are regarded as alert conditions.
FIG. 2D, in particular, illustrates a tailgating scenario. In this
scenario, two trajectories 212 and 214 are observed to diverge from
door zone 231 and/or from swipe zone 232. This pattern implies that
two individuals came through the door at substantially the same
time. This pattern, taken in conjunction with the fact that only a
single card had been swiped through exterior card reader 24, would
give rise to a strong inference that tailgating had occurred.
[0036] FIG. 2E depicts a piggy-backing scenario. Here, a person
approaches swipe zone 232 and possibly door zone 231 along
trajectory 222. The same individual departs from zones 231/232
along a return trajectory 224 at the same time that another
individual appears in door zone 231 and moves away in a different
direction along trajectory 225. This area-related pattern, taken in
conjunction with the fact that only a single card swiping had
occurred--at interior card reader 25--would give rise to a strong
inference that the first person had approached door 21 from inside
secure area 23 and caused it to become unlocked it in order to
allow a second person to enter, i.e., piggy-backing had
occurred.
[0037] FIG. 2E depicts a loitering scenario. Here, a person
approaches swipe zone 232 and possibly door zone 231 along
trajectory 226. The same individual departs from zones 231/232
along a return trajectory 228. Approaching so close to door 21
without swiping one's card and going through the door is a
suspicious behavior, especially if repeated, and especially if the
person remains within door zone 231 or swipe zone 232 for periods
of time that tend to be associated with suspicious behavior. This
type of activity suggests that the individual being tracked is, for
example, waiting for a friend, for example, to show up in
non-secure area 22 so that he/she can be let in using the first
person's card. This behavior may be a precursor to a piggy-backing
event that is about to occur once the friend arrives at door
21.
[0038] FIG. 1B illustrates the operation of alert reasoning 132
responsive to top hypothesis 130. By analyzing top hypothesis 130,
a restricted zone determination module 146 of alert reasoning 132
can determine whether a particular zone, or sub-zone within some
larger overall space, has been entered, such as door area 131 or
swipe area 132. At the same time, a determination is made at a
multiple entries determination module 160 whether there were
multiple entries during an event, e.g., when tailgating is the
event sought to be discovered. Since the system has the complete
information of the objects' number and motion history, it can
record activities or traffic information 162 in a database 152 when
multiple entries are not detected and no violation is recorded. The
system can also include an unattended object module 148, which can
determine from top hypothesis 130 whether a non-human object
appeared within the area under surveillance and was left there.
This could be detected by observing a change in the background
information. Such an event may also be recorded in an activity
recorder 162 as following the alert rules and occurring with high
likelihood, but as not being a violation to be recorded at the
violation recorder 150 in the database 152. Again, a user such as a
review specialist may query 154 the database 152 and access
recorder events through a user interface 156 for viewing at a
monitor 158. The violations recorded in the violation recorder 150
would likely have higher priority to security personnel if
tailgating, for example, is the main problem sought to be
discovered, whereas activities merely recorded in the activity
recorder 162 may be reviewed for traffic analysis and other data
collection purposes.
[0039] FIG. 1C is a flow diagram illustrating the operation of
alert reasoning 132 in detecting the occurrence of tailgating or
piggy-backing responsive to top hypothesis 130. The number of
trajectories in the top hypothesis is N. The track number is
started at i=0 at 172. When it is determined at 173 that not all of
the top tracks have yet been run through the alert reasoning
module, then the process proceeds to 174. At 174, it is determined
whether the length of the ith track is greater than a minimum
length. If it is not, this means that the track is not long enough
to be confirmed as indeed a real track, in which case the process
moves to increment to the next track in the list at 183. If the ith
track is determined to be greater than the minimum length, it is
determined whether the ith track is a "coming in" track at 175.
"Coming in" track means that the motion direction of the track is
from door zone 231 or from non-secure area 22 into secure area 23.
If it is not, the process goes to 183 to check next track if there
is one. Otherwise, at 176, it is determined whether a card was
swiped. If it was, there is no alert and the process moves to 183.
If there was no swipe, then it is determined at 177 whether a
person on another track swiped a card. If not, the alert code is
designated "unknown" at 178 because although there was an entry
without a swipe, such entry does not fit the tailgating or
piggy-backing scenarios and the alert code is communicated to
return alert code processing at 179. If there was a swipe, it is
determined at 180 whether a "coming in" time difference is less
than a time Td. This parameter is a number that can be determined
heuristically and can be, for example, the maximum allowed time
difference between when a door opens and closes with one card
swipe. If the coming in time is greater than Td, then piggy-backing
is suspected and designated at 181 and the piggy-backing alert code
is communicated to return alert code processing at 179. If the
"coming in" time difference is determined to be less than Td, a
tailgating alert code is designated at 182 and the tailgating alert
code is communication to alert code processing at 179. It is likely
that tailgating occurred in this situation because this means that
someone on another track had just swiped a card and had entered and
possibly left the door open for the person on the "coming in"
track.
[0040] The following table is a list of alert conditions, including
tailgating and piggy-backing, that the system may be programmed to
detect. It will be seen from this table that, although not shown in
FIGS., it is possible to detect certain alert conditions using a
camera whose area under surveillance is the non-secure area, e.g.,
area 22. The table uses the following symbols: A=Person A; B=Person
B; L(A)=Location of person A; L(B)=Location of person B; S=Secure
Area; N=Non-Secure Area.
1 Alert Condition Definition Scenario Camera Entry More than L(A) =
N, L(B) = N; N or S Tailgating one person enters secure area A
cards in; on single entry card. L(A) = S, L(B) = S. Reverse One
person L(A) = S, L(B) = N; N or S Entry Tailgating enters the
secure area while A cards out; another exits on a single exit L(A)
= N, L(B) = S. card. Entry One person L(A) = N, L(B) = N; N
Collusion uses card to allow another A cards in; person to enter
without L(A) = N, L(B) = S. entering himself. Entry on Person in
L(A) = S, L(B) = N; S Exit Card secure area uses card to allow A
cards out; (Piggybacking) another person to enter without L(A) = S,
L(B) = S. leaving himself. Failed Person in L(A) = N; A N
Entry/Loitering non-secure area tries to use a unsuccessfully
attempts to card in at Entry card to open door and fails to gain
entry. Loitering Person in L(A) = N; S in Secure Area secure area
goes to door zone but does not go through
[0041] In determining whether a particular one of these scenarios
has occurred, the system uses a) trajectory length, trajectory
motion over time and trajectory direction derived from the top
hypothesis and b) four time measurements. The four time measures
are enter-door-time, leave-door time, enter-swipe-time and
leave-swipe time. These are, respectively, the points in time when
a person is detected as having entered the door zone, left the door
zone, entered the swipe and left the swipe zone, respectively. In
this embodiment the system does not have access to electronic
signals associated with the door opening/closing or with card
swiping. The computer that carries out the invention is
illustratively different from, and not in communication with, the
computer that validates the card swiping data and unlocks the door.
Thus in the present embodiment, the fact that someone may have
opened the door or swiped their card is inferred based on their
movements. Thus the designations "A cards in" and "A cards out" in
the scenarios are not facts that are actually determined but rather
are presented in the table as a description of the behavior that is
inferred from the tracking/timing data.
[0042] As described above relative to FIG. 1C, timing also plays a
role in the applying at least some of the scenarios shown in the
table in that people must enter and/or leave certain zones within
certain time frames relative to each other in order for their
movements to be deemed suspicious. Thus in order to decide, based
on data from a camera in the secure area, that Entry Tailgating may
have occurred, the difference between door-entry-time for one
person and the door-entry-time for another person must be less than
Td. That is, people who enter at times that are very far apart are
not likely to be guilty of tailgating. If the camera is in the
non-secure zone, the difference between door-leave-time for one
person and the door-leave-time for another person must be less than
Td.
[0043] The timing for Reverse Entry Tailgating requires that one
person's door-leave-time is relatively close to another person's
enter-door-time.
[0044] The timing for Piggybacking is that one person's enter time
is close to another person's enter-swipe-time and, in fact, is less
than Td.
[0045] The timing for Failed Entry/Loitering at Entry as well as
for Loitering in Secure Area is that a person is seen in the swipe
zone for at least a minimum amount of time, combined with the
observance of a U-turn type of trajectory, i.e., the person
approached the swipe zone, stayed there and then turned around and
left the area.
[0046] In any of these scenarios in which the behavior attempted to
be detected involves observing that a person has entered either the
door zone or the swipe zone, the time that the person spends in
that zone needs to be greater than some minimum so that the mere
fact that someone quickly passes through a zone--say the swipe zone
within the secure area--on their way from one part of the secure
zone to another will not be treated as a suspicious occurrence.
Image Processing and Hypothesis Overview
[0047] Returning again to FIG. 1A, the basic components of image
processing 103, leading to the generation of top hypothesis 130,
are shown. In particular, the information in each video frame is
digitized 104 and a background subtraction process 106 is performed
to separate background from foreground, or current, information.
The aforementioned frame rate of 10 frames/second can be achieved
by running camera 102 at that rate or, if the camera operates at a
higher frame rate, by simply capturing and digitizing only selected
frames.
[0048] Background information is information that does not change
from frame to frame. It therefore principally includes the physical
environment captured by the camera. By contrast, the foreground
information is information that is transient in nature. Images of
people walking through the area under surveillance would thus show
up as foreground information. The foreground information is arrived
at by subtracting the background information from the image. The
result is one or more clusters of foreground pixels referred to as
"blobs."
[0049] Each foreground blob 108 is potentially the image of a
person. Each blob is applied to a detection process 110 that
identifies human forms using a convolutional neural network that
has been trained for this task. More particularly, the neural
network in this embodiment has been trained to recognize the head
and upper body of a human form. The neural network generates a
score, or probability, indicative of the probability that the blob
in question does in fact represent a human. These probabilities
preferably undergo a non-maximum suppression in order to identify a
particular pixel that will be used as the "location" of the object.
A particular part of the detected person, e.g., the approximate
center of the top of the head, is illustratively used as the
"location" of the object within the area under surveillance.
Further details about the neural network processing are presented
hereinbelow.
[0050] Other object detection approaches can be used. As but one
example, one might scan the entire image on a block-by-block or
other basis and apply each block to the neural network in order to
identify the location of humans, rather than first separating
foreground information from background information and only
applying foreground blobs to the neural network. The approach that
is actually used in this embodiment, as described above, is
advantageous, however, in that it reduces the amount of processing
required since the neural network scoring is applied only to
portions of the image where the probability of detecting a human is
high.
[0051] On the other hand, certain human objects that were detected
in previous frames may not appear in the current foreground
information. For example, if a person stopped moving for a period
of time, the image of the person may be relegated to the
background. The person will then not be represented by any
foreground blob in the current frame. One way of obviating this
problem was noted above: simply apply the entire image,
piece-by-piece, to detection process 110 rather than applying only
things that appear in the foreground. But, again, that approach
requires a great deal of additional processing.
[0052] The system addresses this issue by supplying detection
process 110 with the top hypothesis 130, as shown in FIG. 1A at
134. Based on the trajectories contained in the top hypothesis, it
is possible to predict the likely location of objects independent
of their appearance in the foreground information. In particular,
one would expect to detect human objects at locations in the
vicinity of the ending points of the top hypothesis's trajectories.
Thus in addition to processing foreground blobs, detection process
110 processes clusters of pixels in those vicinities. Any such
cluster that yields a high score from the neural network can be
taken as a valid human object detection, even if not appearing the
foreground. This interaction tightly integrates the object
detection and tracking, and makes both of them much more
reliable.
[0053] The object detection results 112 are refined by optical flow
projection 114. The optical flow computations involve brightness
patterns in the image that move as the detected objects that are
being tracked move. Optical flow is the apparent motion of the
brightness pattern. Optical flow projection 114 increases the value
of the detection probability (neural network score) associated with
an object if, through image analysis, the detected object can, with
a high degree of probability, be identified to be the same as an
object detected in one or more previous frames. That is, an object
detected in a given frame that appears to be a human is all the
more likely to actually be a human if that object seems to be the
displaced version of a human object previously detected. In this
way, locations with higher human detection probabilities are
reinforced over time. Further details about optical flow projection
can be found, for example, in B. T. P. Horn, Robot Vision, M.I.T.
Press 1986.
[0054] The output of optical flow projection 114 comprises data 118
about the detected objects, referred to as the "object detection
data." This data includes not only the location of each object, but
its detection probability, information about its appearance and
other useful information used in the course of the image processing
as described below.
[0055] The data developed up to any particular point in time, e.g.,
a point in time associated with a particular video frame, will
typically be consistent with multiple different scenarios as to a)
how many objects of the type being tracked, e.g., people, are in
the area under surveillance at that point in time and b) the
trajectories that those objects have followed up to that point in
time. Hypothesis generation 120 processes the object detection data
over time and develops a list of hypotheses for each of successive
points in time, e.g., for each video frame. Each hypothesis
represents a particular unique interpretation of the object
detection data that has been generated over a period of time. Thus
each such hypothesis comprises a particular number, and the
locations, of objects of the type being tracked that, for purposes
of that hypothesis, are assumed to be then located in the area
under surveillance, and b) a particular assumed set of
trajectories, or tracks, of that detected objects have
followed.
[0056] As indicated at 124, each hypothesis is given a score,
referred to herein as a likelihood, that indicates the likelihood
that that particular hypothesis is, indeed, the correct one. That
is, the value of each hypothesis's likelihood is a quantitative
assessment of how likely it is that a) the objects and object
locations specified in that hypothesis are the objects locations of
the objects that are actually in the area under surveillance and b)
the trajectories specified in that hypothesis are the actual
trajectories of the hypothesis's objects.
[0057] Hypothesis management 126 then carries out such tasks as
rank ordering the hypotheses in accordance with their likelihood
values, as well as other tasks described below. The result is an
ordered hypothesis list, as indicated at 128. The top hypothesis
130 is the hypothesis whose likelihood value is the greatest. As
noted above, the top hypothesis is then used as the input for alert
reasoning 132.
[0058] The process then repeats when a subsequent frame is
processed. Hypothesis generation 120 uses the new object detection
data 118 to extend each hypothesis of the previously generated
ordered hypothesis list 128. Since that hypothesis list is the most
recent one available at this time, it is referred to herein as the
"current hypothesis list." That is, the trajectories in each
hypothesis of the current hypothesis list are extended to various
ones of the newly detected objects. As previously noted, the object
detection data developed for any given frame can almost always
support more than one way to correlate the trajectories of a given
hypothesis with the newly detected objects. Thus a number of new
hypotheses may be generated, or "spawned," from each hypothesis in
the current hypothesis list.
[0059] It might be thought that what one should do after the
hypotheses have been rank-ordered is to just retain the hypothesis
that seems most likely--the one with the highest likelihood
value--and forget about the rest. However, further image detection
data developed in subsequent frames might make it clear that the
hypothesis that seemed most likely--the present "top
hypothesis"--was in error in one or more particulars and that some
other hypothesis was the correct one.
[0060] More particularly, there are many uncertainties in carrying
out the task of tracking multiple objects in a area under
surveillance if single frames are considered in isolation. These
uncertainties are created by such phenomena as false detections,
missing data, occlusions, irregular object motions and changing
appearances. For example, a person being tracked may "disappear"
for a period of time. Such disappearance may result from the fact
that the person was occluded by another person, or because the
person being tracked bent over to tie her shoelaces and thus was
not detected as a human form for some number of frames. In
addition, the object detection processing may generate a false
detection, e.g., reporting that a human form was detected a
particular location when, in fact, there was no person there. Or,
the trajectories of individuals may cross one another, creating
uncertainty as to which person is following which trajectory after
the point of intersection. Or people who were separated may come
close together and proceed to walk close to one another, resulting
in the detection of only a single person when in fact there are
two.
[0061] However, by maintaining multiple hypotheses of object
trajectories, temporally global and integrated tracking and
detection are achieved. That is, ambiguities and uncertainties can
be generally resolved when multiple frames are taken into account.
Such events are advantageously handled by postponing decisions as
to object trajectories--through the mechanism of maintaining
multiple hypotheses associated with each frame--until sufficient
information is accumulated over time.
[0062] An example involving the hypothesis shown in FIG. 3A that
was introduced and hereinabove shows how such contingencies can
lead to different hypotheses.
[0063] In particular, as previously noted, FIG. 3A depicts an
hypothesis is associated with a video frame, identified by the
frame number i+4. As seen in the rightmost portion of the FIG.,
four detected objects, represented by respective ones of graphical
nodes 302 were detected in frame i+4 and it has been determined--by
having tracked those objects through previous frames, including
frames i, i+1, i+2 and i+3--that those objects followed the
particular trajectories formed by the connections 304 from one
frame to the next.
[0064] An individual one of connections 304 is an indication that,
according to the particular hypothesis in question, the two linked
nodes 302 correspond to a same object appearing and being detected
in two temporally successive frames. The manner in which is this
determined is described at a more opportune point in this
description.
[0065] To see how the hypothesis represented in FIG. 3A was
developed, we turn our attention back to frame i. In particular,
this hypothesis had as its progenitor in one of the list of
hypotheses 128 that was developed for frame i. That hypothesis
included four detected objects A, B, C and D and also included a
particular set of trajectories 301 that those objects were
hypothesized to have followed up through frame i. The four objects
A through D are shown in straight vertical line only because the
FIG. is a combination spatial and temporal representation. Time
progresses along the x axis and since those four objects were
detected in frame i, they are vertically aligned in the FIG. In
actuality, the objects detected in a given frame can appear in any
location with the area under surveillance.
[0066] The reason that the objects detected in a given frame are
given different letter designations from those in other frames is
that it is not known to a certainty which objects detected in a
given frame are the same as which objects detected in previous
frames. Indeed, it is the task of the multiple-hypothesis
processing disclosed herein to ultimately figure this out.
[0067] Some number of objects 302 were thereafter detected in frame
i+1. It may have been, for example, four objects. However, let it
be assumed that the object detection data for frame i+1 is such
that a reasonable scenario is that one of those four detections was
a false detection. That is, although optical flow projection 114
might have provided data relating to four detected objects, one of
those may have been questionable, e.g., the value of its associated
detection probability was close to the borderline between person
and non-person. Rather than make a final decision on this point,
the multiple-hypothesis processing entertains the possibility that
either the three-object or the four-object scenario might be the
correct one. Hypothesis processing associated with frames following
frame i+1 can resolve this ambiguity.
[0068] It is the three-object scenario that is depicted in FIG. 3A.
That is, it is assumed for purposes of the particular hypothesis
under consideration that there were only three valid object
detections in frame i+1: E, F and G. Moreover, the processing for
this has proceeded on the theory that object E detected at frame
i+1 is the same as object A detected at frame i. Hence this
hypothesis shows those objects as being connected. The scenario of
this hypothesis further includes a so-called merge, meaning that
both of the objects B and C became object F. This could happen if,
for example, object B walked "behind" (relative to camera 102)
object C and was thus occluded. The scenario further has object G
being the same as object D.
[0069] As we will see shortly, the scenario depicted in FIG. 3A,
the above is but one of several possible trajectory stories
explaining the relationship between objects A through D detected in
frame i and objects E though G detected in frame i+1.
[0070] Proceeding to frame i+2, the object detection data from
optical flow projection 114 has provided as one likely scenario the
presence of five objects H through L. In this hypothesis, objects H
and J both emerged from object E that was detected in frame i+1.
This implies that both objects A and E represent two people walking
closely together, but were not distinguishable as being two people
until frame i+2. Objects K and L are hypothesized as being the same
as objects F and G. Object I is hypothesized as being a newly
appearing object that hadn't followed any of the previously
identified trajectories, this being referred to as a trajectory
initialization.
[0071] Four objects M through P were detected in frame i+3. The
hypothesis of FIG. 3A hypothesizes that objects M, O and P detected
in frame i+3 are objects I, L and J, respectively, detected in
frame i+2. Thus respective ones of the connections 304 extend the
trajectories that had ended at objects I, L and J in frame i+2 out
to objects M, O and P, respectively in frame i+3. The scenario
represented by this hypothesis does not associate any one of the
detected objects M through P with either object H or object K. This
can mean either that one or both of the objects H and K a) have
actually disappeared from the area under surveillance or that b)
they are actually in the area under surveillance but, for some
reason or another, the system failed to detect their presence in
frame i+3. These possibilities are not arrived at arbitrarily but,
rather, based on the certain computations that make them
sufficiently possible as to not being able to be ruled out at this
point. Moreover, the scenario represented by this hypothesis does
not associated object N with any of the objects detected in frame
i+2. Rather, the scenario represented by this hypothesis embodies
the theory that object N is a newly appearing object that initiates
a new trajectory.
[0072] In frame i+4, four objects Q through T are detected. The
object detection data associated with these objects supports a set
of possible outcomes for the various trajectories that have been
being tracked to this point and the hypothesis. The scenario of
FIG. 3A is a particular one such set of outcomes. In particular, in
this hypothesis objects R, S and T are identified as being objects
M, O and P detected in frame i+3. The object detection data also
supports the possibility that object Q is actually object H,
meaning that, for whatever reason, object H was not detected in
frame i+3. For example, the person in question may have bent down
to tie a shoelace and therefore did not appear to be a human form
in frame i+3. The data further supports the possibility that none
of the objects Q through T is the same as object N. At this point
object N would appear to have been a false detection. That is, the
data supports the conclusion that although optical flow projection
114 reported the presence of object N, that object did not actually
exist. The data further supports the possibility that none of the
objects Q through T is the same as object K. At this point object K
would appear to truly have disappeared from the area under
surveillance.
[0073] All of the foregoing, it should be understood, is only one
of numerous interpretations of what actually occurred in the area
under surveillance over the frames in question. At each frame, any
number of hypothesis can be spawned from each hypothesis being
maintained for that frame. In particular, the data that supported
the scenario shown in FIG. 3A leading to the hypothesis shown for
frame i+4 was also supportive of a different scenario, leading to
many other hypotheses for frame i+4.
[0074] FIG. 3B shows one such alternative scenario. In particular,
the data in frame i+1 supported the possibility that the trajectory
of object B merged into object E instead of into object F, leading
to a different hypothesis for frame i+1 in which that merger is
assumed. Moreover, the data for frame i+2 supported the possibility
that object I was a false detection. Thus the depicted chain of
hypothesis does not include object I at all. The data for frame i+3
supported the possibility that object M, rather than being the same
as object I, was really object H and that objects O and P were
actually objects J and L instead of the other way around. The data
for frame i+3 also supported the possibility that object Q was a
false detection.
[0075] FIG. 4 is a more generalized picture illustrating the
process by which each of the hypotheses generated for a particular
frame can spawn multiple hypotheses and how the total number of
hypotheses is kept to manageable levels. It is assumed in this
example, that the hypothesis list for a certain ith frame contains
only one hypothesis. For example, after a period of time when no
human objects were detected, a single human form appears in frame
i. The single hypothesis, denominated A, associated with this frame
contains that single object and no associated trajectory, since
this is the first frame in which the object is detected. Let us
assume that in the next frame i+1, two objects are detected. Let us
also assume that the object detection data for frame i+1 supports
two possible hypotheses, denominated AA and AB. Hypothesis AA
associates the originally detected person with one of the two
people appearing in frame i+1. Hypothesis AB associates the
originally detected person with the other of the two people
appearing in frame i+1. Hypothesis AA is at the top of the
hypothesis list because, in this example, its associated likelihood
is greater than that associated with hypothesis AB.
[0076] In frame i+2 some number of objects are again detected. Even
if only two objects are detected, the data may support multiple
scenarios associating the newly detected objects with those
detected in frame i+2. It is possible that neither of the two
people detected in frame i+2 is the one detected in frame i. That
is, the person detected in frame i may have left the area under
surveillance and yet a third person has appeared. Moreover, each of
the people detected in frame i+2 might be either of the people that
were detected in frame i+1. Thus each of the hypotheses AA and AB
can, in turn, give rise to multiple hypotheses. In this example,
hypothesis AA gives rise to three hypotheses AAA AAB, and AAC and
hypothesis AB gives rise to four hypotheses ABA, ABB, ABC and ABD.
Each of those seven hypotheses has its own associated likelihood.
Rank ordering them in accordance with their respective likelihoods
illustratively has resulted in hypothesis AAA being the top
hypothesis, followed by ABA, ABB, AAB, AAC, ABC and ABD.
[0077] The process proceeds similarly through successive frames.
Note how in frame i+3, the top hypothesis ABAA did not originate
from the hypotheses that was the top hypothesis in frames i+1 and
i+2. Rather, it has eventuated that frame i+3's top hypothesis
evolved from the second-most-likely hypotheses for frame i+1, AB,
and the second-most-likely hypotheses from frame i+2, ABA. In this
way, each of the multiple hypotheses is either reinforced,
eliminated or otherwise maintained as frames are sequentially
analyzed over time.
[0078] Inasmuch as the data developed in each frame can support
multiple extensions of each of the hypotheses developed in the
previous frame, the total number of hypotheses that could be
generated could theoretically grow without limit. Thus another
function of hypothesis management 126 is to prune the hypothesis
list so that the list contains only a tractable number of
hypotheses on an ongoing basis. For example, hypothesis management
126 may retain only the M hypotheses generated by hypothesis
generation 120 that have the highest likelihood values. Or
hypothesis management 126 may retain only those hypotheses whose
likelihood exceeds a certain threshold.
[0079] In the example of FIG. 4, only the top 12 hypotheses are
retained. Thus it is seen that none of the hypotheses that spawned
from the two lowest-ranking hypotheses in frame i+2--ABC and
ABD--have made the top-twelve list in frame i+3. And in frame i+4,
the top 12 hypotheses evolved from only the top six hypothesis in
frame i+3's hypothesis list.
Hypothesis Generation, Likelihood Generation and Hypothesis
Management
[0080] With the foregoing as an overview, we are now in a position
to see how the hypotheses are generated from one frame to the next,
how the likelihoods of each hypothesis are computed, and how the
hypotheses are managed.
[0081] Given a particular trajectory within a given hypothesis, one
must consider the possibility that that trajectory connects to any
one or more of the objects detected in the present frame, the
latter case being a so-called split as seen in FIGS. 3A and 3B. One
must also consider the possibility that the trajectory in question
does not connect to any of the objects detected in the present
frame--either because the object that was on the trajectory has
left the area under surveillance or because it has not left the
area under surveillance but was not detected in this particular
frame.
[0082] Moreover, given a particular object detected in the current
frame, one must consider the possibility that that object connects
to any one or more of the trajectories of a given hypothesis, the
latter case being a so-called merge as seen in FIGS. 3A and 3B. One
must also consider the possibility that object in question does not
connect to any of the trajectories of the given hypothesis, meaning
that the object has newly appeared in the area under surveillance
and a new trajectory is being initiated. One must also consider the
possibility that the detected object does not actually exist, i.e.,
the detection process has made an error.
[0083] The various "connection possibilities" just mentioned can
occur in all kinds of combinations, any one of which is
theoretically possible. Each combination of connection
possibilities in the current frame associated with a given
hypothesis from the previous frame potentially gives rise to a
different hypothesis for the current frame. Thus unless something
is done, the number of hypotheses expands multiplicatively from one
frame to the next. It was noted earlier in this regard that
hypothesis management 126 keeps the number of hypotheses in the
hypothesis list down to a manageable number by pruning away the
hypotheses generated for a given frame with relatively low
likelihood values. However, that step occurs only after a new set
of hypotheses has been generated from the current set and the
likelihoods for each new hypothesis has been computed. The amount
of processing required to do all of this can be prohibitive if one
generates all theoretically possible new hypotheses for each
current hypothesis.
[0084] However, many of the theoretically possible hypothesis are,
in fact, quite unlikely to be the correct one. The present
invention prevents those hypotheses from even being generated by
rejecting unlikely connection possibilities at the outset, thereby
greatly reducing the number of combinations to be considered and
thus greatly reducing the number of hypotheses generated. Only the
possibilities that remain are used to form new hypotheses. The
process of "weeding out" unlikely connection possibilities is
referred to herein as "local pruning."
[0085] FIGS. 5 and 6 show a process for carrying out the foregoing.
Reference is first made, however, to FIG. 7, which shows a
simplified example of how hypotheses are generated.
[0086] In particular, FIG. 7 illustrates frame processing for
frames i-1, i, and i+1. In order to keep the drawing simple, a
simplifying assumption is made that only the top two hypotheses are
retained for each frame. In actual practice any workable scheme for
keeping the number of hypotheses to a useable level may be used,
such as retaining a particular number of hypotheses, or retaining
all hypotheses having a likelihood above a particular value. The
latter value might itself being varied for different frames,
depending on the complexity of the content observed within the
frames.
[0087] As processing begins for frame (i-1), shown in the first row
of FIG. 7, it is assumed that only one hypothesis survived from the
previous frame i-2. That hypothesis contains one trajectory 71. It
is also assumed that only one object 72 was detected in the frame
i-1. There are thus only three possible hypotheses for the frame
i-1 referred to in FIG. 7 as "potential hypotheses," stemming from
the previous hypothesis. In hypothesis A, object 72 actually
connects to trajectory 71. In hypothesis B, object 72 does not
connect to the trajectory but, rather, initiates a new trajectory.
In hypothesis C, the detection was a false detection, so that
object 72 does not exist in hypothesis C. Note that hypothesis C
also takes account of another connection possibility that is always
theoretically possible--namely that trajectory 71 does not connect
to any objects detected in the current frame.
[0088] The processing is based on a parameter referred to as a
connection probability ConV computed for each detected
object/trajectory pair. The connection probability, more
particularly, is a value indicative of the probability that the
detected object is the same as the object that terminates a
particular trajectory. Stated another way, the connection
probability is indicative of the likelihood that the detected
object is on the trajectory in question. The manner in which ConV
can be computed is described below.
[0089] As the processing proceeds, it is determined, for each
connection probability ConV, whether it exceeds a so-called
"strong" threshold Vs, is less than a so-called "weak" threshold Vw
or is somewhere in between. A strong connection probability ConV,
i.e., ConV>Vs, means that it is very probable that the object in
question is on the trajectory in question. In that case we do not
allow for the possibility that the detected object initiates a new
trajectory. Nor do we allow for the possibility that the detected
object was a false detection. Rather we take it as a given that
that object and that trajectory are connected. If the connection
probability is of medium strength--Vw<ConV<Vs--we still allow
for the possibility that the object in question is on the
trajectory in question, but we also allow for the possibility that
the detected object initiates new trajectory as well as the
possibility that there was a false detection. A weak ConV, i.e.,
ConV<Vw means that it is very improbable that the object in
question is on the trajectory in question. In that case we take it
as a given that they are not connected and only allow for the
possibility that the detected object initiates new trajectory as
well as the possibility that there was a false detection.
[0090] In the present case, we assume a strong connection between
the terminating object of trajectory 71 and object 72. That is,
ConV>Vs. As just indicated, this means that the probability of
object 72 being the object at the end of trajectory 71 is so high
that we do not regard it as being at all likely that object 72 is a
newly appearing object. Therefore, potential hypothesis A is
retained and potential hypothesis B is rejected. As also just
noted, the processing does not allow initializations for strong
connections or false detections. Therefore potential hypothesis C
is rejected as well. The process of rejecting potential hypotheses
B and C is what is referred to hereinabove as "local pruning." The
ordered hypothesis list thus includes only hypothesis A.
[0091] As processing begins for frame i, shown in the second row of
FIG. 7, we have only the one hypothesis--hypothesis A--from the
previous frame to work with. That hypothesis contains one
trajectory 73. However, two objects 74 and 75 are detected in this
frame. There are thus more connection possibilities. In particular,
we have for object 74 the possibility that it connects to
trajectory 73; that it starts its own trajectory; and that it was a
false detection. We have the same possibilities for object 75. We
also must consider various combinations of these, including the
possibility that both objects 74 and 75 connect to trajectory 73.
We also have the possibility that trajectory 73 does not connect to
either of objects 74 and 75. There are thus a total of nine
potential hypotheses AA, AB, AC, AD, AE, AF, AG, AH and AI. The
depiction of overlapping trajectory nodes of potential hypothesis
AD is indicative of the fact that this potential hypothesis
comprises two trajectories both of which are extensions of
trajectory 73 and which split at the ith frame.
[0092] Objects 74 and 75 have respective connection probabilities
ConV1 and ConV2 with the terminating object of trajectory 73.
Different combinations of these two values will generate different
local pruning results. We assume ConV1 is very strong
(ConV1>Vs). As a result, any potential hypotheses in which
object 74 is not present or in which object 74 starts its own
trajectory do not survive local pruning, these being hypotheses AB,
AC, AE, AF, AH and AI. Thus at best only hypotheses AA, AD and AG
survive local pruning. Assume, however, that ConV2 is neither very
strong nor very weak. That is Vw<ConV2<Vs. In this case we
will entertain the possibility that object 75 is connected to
trajectory 73 but we do not rule out the possibility that it starts
its own hypothesis or that was a false detection. Thus of the
hypotheses AA, AD and AG remaining after considerations relating to
object 74, none of those potential hypotheses are rejected after
considering object 75. If ConV2 had been greater than Vs; only
hypothesis AD would have survived local pruning.
[0093] It is assumed that hypotheses AD and AG had the two highest
likelihood values. Thus they are the two hypotheses to be retained
in the hypothesis list for frame i.
[0094] As processing begins for frame i+1, shown in the third row
of FIG. 7, we have two hypotheses--hypotheses AD and AG--from the
previous frame to work with. It is assumed that only one object was
detected in this frame. The potential hypotheses include hypotheses
that spawn both from hypothesis AD and from hypothesis AG. Each of
the hypotheses AD and AG can potentially spawn five hypotheses in
frame i+1.
[0095] Considering first hypothesis AD, which comprises
trajectories 76 and 77, it will be seen that object 80 can
potentially connect to the terminating object of trajectory 76
(potential hypothesis ADA), to the terminating object of trajectory
77 (ADB), to the terminating object of both trajectories (ADC) or
to neither (ADD). In addition, object 80 could potentially be a
false detection (ADE). So there are a total of five hypotheses that
potentially could derive from hypothesis AD.
[0096] Hypothesis AG also comprises two trajectories. One of these
is the same upper trajectory 76 as is in hypothesis AD. The other
is a new trajectory 79 whose starting node is object 75. Thus in a
similar way a total of five hypotheses can potentially derive from
hypothesis AG--AGA, AGB, ABC, AGD and AGE.
[0097] Note that the objects that terminate the two trajectories of
potential hypothesis AD are the same objects that terminate the two
trajectories of potential hypothesis AG. These are, in fact,
objects 74 and 75. Let ConV1 represent the connection probability
between the terminating object of trajectory 76 and detected object
80. Let ConV2 indicate the connection probability between object 80
and the terminating objects of trajectories 77 and 79 (both of
which are object 75). First, assume ConV1 is neither too strong
(>Vs) nor too weak (<Vw). Therefore, ADA, AGA, ADD, AGD, ADE,
AGE survive local pruning. Next, assume ConV2<Vw. In this case
ADB, ADC, AGB, and AGC do not survive local pruning.
[0098] Note that a difference between hypotheses AD and AG, which
are the survivors at the end of frame i, is based on whether object
75 is connected to the prior trajectory or not. Therefore, in frame
i+1, it may be that if the 1 st, 4th and 5th potential hypotheses
derived from AD are the ones that survive local pruning (that is
ADA, ADD and ADE survive), then the 1.sup.st, 4th and 5th
hypotheses derived from AG would also survive (that is AGA, AGD and
AGE). This is because local pruning is only concerned with the
connection probability between two objects. The question of whether
object 75 is or is not connected to the prior trajectory is not
taken into account.
[0099] The various factors that go into computing the likelihoods
for the various hypotheses are such that even if ADA has the
highest likelihood, this does not necessary mean that AGA has the
next highest likelihood. In this example, in fact, AGD has the
highest likelihood value and so it survives while AGA does not.
[0100] Returning now to FIG. 5, this FIG. shows a process carried
out by hypothesis generation 120 (FIG. 1A) in order to implement
the local pruning just described.
[0101] It is assumed in FIG. 5 that there are M hypotheses in the
current hypothesis list, i.e., the hypothesis list that was
generated based on the data from the previous frame. The various
hypotheses are represented by an index j, j=0, 1, 2, . . . (M-1).
The process of FIG. 5 considers each of the M hypotheses in turn,
beginning with j=0 as indicated at 501. At this time j<M. Thus
the process proceeds through 502 to 503.
[0102] Each hypothesis illustratively comprises N trajectories, or
tracks, where the value of N is not necessarily the same for each
hypothesis. The various trajectories of the jth hypothesis are
represented by an index i, i=0, 1, 2 . . . (N-1). Index i is
initially set to 0 at 503. At this time i<N. Thus the process
proceeds through 504 to 506. At this point we regard it as possible
that the object following the ith track will not be detected in the
current frame. We thus set a parameter referred to as
ith-track-missing-detection to "yes."
[0103] There are illustratively K objects detected in the current
frame. Those various objects are represented by an index k, where
k=0, 1, 2, . . . (K-1). In a parallel processing path to that
described so far, index k is initially set to 0 at 513. Since
k<K at this time, the process proceeds through 516 to 509. At
this point we regard it as possible that that the kth object may
not be connected to any of the trajectories of the jth hypothesis
and we also regard it as possible that the kth object may be a
false detection. We thus set the two parameters
kth-object-new-track and kth-object-false-detection to the value
"yes."
[0104] The ith track and the kth object are considered jointly at
511. More particularly, their connection probability ConV is
computed. As will be appreciated from the discussion above, there
are three possibilities to be considered: ConV>Vs,
Vw<ConV<Vs, and ConV<Vw.
[0105] If ConV>Vs, that is the connection probability is strong,
processing proceeds from decision box 521 to box 528. It is no
longer possible--at least for the hypothesis under
consideration--that the ith track will have a missed detection
because we take it as a fact that the kth object connects to the
ith trajectory when their connection probability is very strong. In
addition the strong connection means that we regard it as no longer
possible that the kth object starts a new track or that the kth
object was a false detection. Thus the parameters as
ith-track-missing-detection, kth-object-new-track and
kth-object-false-detection are all set to "no." We also record at
531 the fact that a connection between the kth object and the ith
track is possible.
[0106] If ConV is not greater than Vs, we do not negate the
possibility that the ith track will have a missed detection, or
that the kth object will start a new track or that the kth object
was a false detection. Thus processing does not proceed to 528 as
before but, rather to 523, where it is determined if ConV<Vw. If
it is not the case that ConV<Vw; there is still a reasonable
possibility that the kth object connects to the ith trajectory and
this fact is again taken not of at 531.
[0107] If ConV<Vw, there is not a reasonable possibility that
the kth object connects to the ith trajectory. Thus box 531 is
skipped.
[0108] The process thereupon proceeds to 514, where the index i is
incremented. Assuming that i<N once again, processing proceeds
through 504 to 506 where the parameter as
ith-track-missing-detection is set to "yes" for this newly
considered trajectory. The connection probability between this next
track and the same kth object is computed at 511 and the process
repeats for this new trajectory/object pair. Note that if the kth
object has a strong connection with any of the trajectories of this
hypothesis, box 528 will be visited at least once, thereby negating
the possibility of the kth object initiating a new track or being
regarded as a false detection--at least for the jth hypothesis.
[0109] Once the process has had an opportunity to consider the kth
object in conjunction with all of the tracks of the jth hypothesis,
the value of k is incremented at 515. Assuming that k<K, the
above-described steps are carried out for the new object.
[0110] After all of the K objects have been considered in
conjunction with all of the trajectories of the jth hypothesis, the
process proceeds to 520 where new hypotheses based on the jth
hypothesis are spawned. The value of j is incremented at 517 and
the process repeats for the next hypothesis until all the
hypotheses have been processed.
[0111] FIG. 6 is an expanded version of step 520, indicating how
the data developed during the processing carried out in FIG. 5 is
used to spawn the new hypotheses. In particular, the new hypotheses
are spawned by forming all extensions of the jth hypothesis having
all possible combinations of object/trajectory pairs, unextended
trajectories, unconnected objects and missing objects, that survive
local pruning. That is, the new hypotheses are spawned by
considering all possible combinations of
[0112] a) the object/trajectory pairs identified at step 531;
[0113] b) unextended trajectories, i.e., trajectories for which the
parameter ith-track-missing-detection retains the value "yes" that
was assigned at 506;
[0114] c) unconnected objects, i.e., objects for which the
parameter kth-object-new-track retains the value "yes" that was
assigned at 509; and
[0115] d) missing objects, i.e., objects for which the parameter
kth-object-false-detection retains the value "yes" that was
assigned at 509.
[0116] It was previously indicated that hypothesis management 126
rank orders the hypotheses according to the their likelihood values
and discards all but the top ones. It also deletes the tracks of
hypotheses which are out-of-date, meaning trajectories whose
objects have seemingly disappeared and have not returned after a
period of time. It also keeps trajectory lengths to no more than
some maximum by deleting the oldest node from a trajectory when its
length becomes greater than that maximum. Hypothesis management 126
also keeps a list of active nodes, meaning the ending nodes, or
objects, of the trajectories of all retained hypotheses. The number
of active nodes is the key number of determining the scale of graph
extension, therefore, a careful managing step assures efficient
computation.
[0117] FIG. 8, more particularly, shows the above-mentioned
processing within hypothesis management 126. The process begins by
retrieving, identifying or accessing the hypothesis list generated
by hypothesis generation 120. These hypotheses 601 are preferably
ordered according to their likelihood or probability of occurrence
at 602. After pruning away the unlikely hypotheses--those with
relatively low likelihood values--M hypotheses remain. An
hypothesis index j is set to 0 at 603. At 604, it is determined if
that all of the hypotheses have been worked through. If not, the
process proceeds to consider the N trajectories of the jth
hypothesis, beginning by setting i=0 at 605. It is then determined
at 607 whether the track stop time--meaning the amount of time that
has passed since a detected object was associated with the ith
track--is greater than a track stop time limit Ts. If it has; the
ith track is deleted at 608 for computational and storage
efficiency, the theory being that the object being tracked was lost
track of or that this was a false track to begin with. If the ith
track stop time is determined not to be greater than Ts; at 609, it
is determined whether the ith track length is greater than maxL. If
the ith track length is determined to be greater than maxL; the
oldest node in the track is deleted at 610, again for computational
and storage efficiency. If the ith track length is determined not
to be greater than maxL at 609; no tracks or nodes in tracks are
deleted and the next operation is 611, which is also the next
operation after track deletion at 608 or node deletion at 610. At
611, the track is incremented, or i=i+1 and the process returns to
606. When all of the N tracks have been worked through; the process
moves on to 612, where it is determined whether the jth hypothesis
still has at least one track. It may be case that the last of its
tracks were deleted at 608. If the jth hypothesis still includes at
least one track; the process goes directly to 613, where the
hypothesis index is incremented and the next hypothesis is
considered. If the jth hypothesis does not include at least one
track, the process goes first to 614 when the jth hypothesis is
deleted before moving on to 613.
[0118] In summary, the design of this multiple object tracking
system follows two principles. First, preferably as many hypotheses
as possible are kept and they are made to be as diversified as
possible to catch all the possible explanations of image sequences.
The decision is preferably made very late to guarantee it is an
informed and global decision. Second, local pruning eliminates
unlikely connections and only a limited number of hypotheses are
kept. This principle helps the system achieve a real-time
computation.
Image Processing Details
[0119] Connection Probability
[0120] Given object detection results from each image, the
hypotheses generation 120 calculates the connection probabilities
between the nodes at the end of the trajectories of each of the
current hypotheses ("maintained nodes") and the new nodes detected
in the current frame. Note that the trajectory-ending nodes are not
necessarily from the previous frame since there may have missing
detections. The connection probability, denoted hereinabove as ConV
is denoted in this section as p.sub.con and is computed according
to, 1 p con = w appear .times. p appear + w pos .times. p pos + w
size .times. p size ( 1 ) where p appear = 1.0 - DistrDist ( hist 1
, hist 2 ) p pos = 1.0 - e - ( x 2 + flow x .times. p flow - x 1
size x 2 ) 2 + ( y 2 + flow y .times. p flow - y 1 size x 2 ) 2 a p
size = 1.0 - e - ( diff x + diff y + diff x - diff y ) 2 b ( 2
)
[0121] Here w.sub.appear, w.sub.pos and w.sub.size are weights in
the connection probability computation. That is, the connection
probability is a weighted combination of appearance similarity
probability, position closeness probability and size or scale
similarity probability. DistrDist is a function to compute
distances between two histogram distributions. It provides a
distance measure between the appearances of two nodes. The
parameters x.sub.1, y.sub.1 and x.sub.2, y.sub.2 denote the
detected object locations corresponding to the maintained node and
the detected node in the current image frame, respectively. The
parameters size.sub.x2, size.sub.y2 are the sizes of the bounding
boxes that surround the various detected objects, in x and y
directions corresponding to the detected node in the current frame.
Bounding boxes are described below. The parameters flow.sub.x,
flow.sub.y represent the backward optical flows of the current
detected node in x and y directions, and p.sub.flow is the
probability of the optical flow which is a confidence measure of
the optical flow computed from the covariance matrix of the current
detected node. Therefore, p.sub.pos measures the distance between
the maintained node (x.sub.1, y.sub.1) and the back projected
location of the current detected node (x.sub.2, y.sub.2) according
to its optical flow (flow.sub.x, flow.sub.y) which is weighted by
its uncertainty (p.sub.flow). These distances are relative
distances between the differences in x and y directions and the
bounding box size of the current detected node. The metric
tolerates larger distance errors for larger boxes. diff.sub.x,
diff.sub.y are the differences in the bounding box size of x and y
directions, respectively. The parameter p.sub.size measures the
size differences between the bounding boxes and penalizes the
inconsistence in size changes of x and y directions. The parameters
a and b are some constants. This connection probability measures
the similarity between two nodes in terms of appearance, location
and size. We prune the connections whose probabilities are very low
for the sake of computation efficiency.
[0122] Likelihood Computation
[0123] The likelihood or probability of each hypothesis generated
in the first step is computed according to the connection
probability of its last extension, the object detection probability
of its terminating node, trajectories analysis and an image
likelihood computation. In particular, the hypothesis likelihood is
accumulated over image sequences, 2 likelihood i = likelihood i - 1
+ j = 1 n - log ( p con j ) - log ( p obj j ) - log ( p trj j ) n +
l img ( 3 )
[0124] where i is the current image frame number, n represents the
number of objects in current hypothesis. The parameter p.sub.conj
denotes the connection probability computed in the first step. If
the jth trajectory has a missing detection in current frame, a
small probability, is assigned to p.sub.conj. The parameter
p.sub.objj is the object detection probability and p.sub.trjj
measures the smoothness of the jth trajectory. We use the average
of multiple trajectories likelihood in the computation. The metric
prefers the hypotheses with better human detections, stronger
similarity measurements and smoother tracks. The parameter
l.sub.img is the image likelihood of the hypothesis. It is composed
of two items,
l.sub.img=l.sub.cov+l.sub.comp (4)
[0125] where 3 l cov = - log ( A ( j = 1 m B j ) + c A + c ) l comp
= - log ( A ( j = 1 m B j ) + c j = 1 m B j + c ) ( 5 )
[0126] Here l.sub.cov calculates the hypothesis coverage of the
foreground pixels and l.sub.comp measures the hypothesis
compactness. A denotes the sum of foreground pixels and B.sub.j
represents the pixels covered by jth node. The parameter m is the
number of different nodes in this hypothesis. .andgate. denotes the
set intersection and .orgate. denotes the set union. The numerators
in both l.sub.cov and l.sub.comp represent the foreground pixels
covered by the combination of multiple trajectories in the current
hypothesis. The parameter c is a constant. These two values give a
spatially global explanation of the image (foreground) information.
They measure the combination effects of multiple tracks in a
hypothesis instead of individual local tracking for each
object.
[0127] More particularly, the hypothesis coverage is a measure of
the extent to which regions of an image of the area under
surveillance that appear to represent moving objects are covered by
regions of the image corresponding to the terminating objects of
the trajectories in the associated hypothesis. Those regions of the
image have been identified, based on their appearance, as being
objects belonging to a particular class of objects, such as people,
and, in addition, have been connected to the trajectories in the
associated hypothesis. The higher hypothesis coverage, the better,
i.e., the more likely it is that the hypothesis in question
represents the actual trajectories of the actual objects in the
area under surveillance. Basically the hypothesis coverage measures
how much of the moving regions is covered by the bounding boxes,
generated by the object detector, corresponding to the end points
of all the trajectories in the associated hypothesis. The
hypothesis compactness is a measure of the overlapping areas
between regions of the image corresponding to the terminating
objects of the trajectories in the associated hypothesis. The less
overlapping area, the higher the compactness. The compactness
measures how compact or efficient the associated hypothesis is to
cover the moving regions. The higher the compactness, the more
efficient, and so the better, is the hypothesis.
[0128] The hypothesis likelihood is a value refined over time. It
makes a global description of individual object detection results.
Generally speaking, the hypotheses with higher likelihood are
composed of better object detections with good image explanation.
It tolerates missing data and false detections since it has a
global view of image sequences.
[0129] There is no computed value of p.sub.conj for a trajectory
that is newly beginning in the current frame or for a trajectory
that is not extended to a newly detected object in the current
frame. It is nonetheless desirable to assign a value of p.sub.conj
for Eq. (3) even in such cases. The probability that those
scenarios are correct, i.e., that a trajectory did, in fact, begin
or end in the current frame, is higher at the edges of the
surveillance field and the door area than in the center because
people typically do not appear or disappear "out of nowhere," i.e.,
in the middle of the surveillance field. Thus an arbitrary,
predefined value for p.sub.conj can be assigned in these
situations. Illustratively, we can assign the value p.sub.conj=1
for detections or terminated trajectories at the very edge of the
surveillance field (including the door zone)m/, and assign
increasingly lower values as one gets closer to the center of the
surveillance field, e.g., in steps of 0.1 down to the value of 0.1
at the very center.
[0130] Object Detection
[0131] Some further details about background subtraction 106 and
detection process 110 will now be presented.
[0132] The object detection itself involves computations of the
probabilities of detecting a human object based upon the image
pixel values. There are many alternatives to the image pixel values
corresponding to head and upper body that may be employed. For
example, the unique way that an object may be walking or the
juxtaposition of a walking human object's legs and/or arms within
image frames may distinguish it from other objects, and generally
any feature of one or more parts of the human body that is
detectable and distinctly identifiable may be employed. In
addition, characteristics of what a human object may be wearing or
otherwise that may be associated, e.g., by carrying, pushing, etc.,
with the moving human object may be used. Particular features of
the human face may be used if resolvable. However, in many
applications such as multiple object detection and tracking in a
area under surveillance of, e.g., over ten meters in each
direction, the single fixed camera and imaging technology being
used may generally not permit sufficient resolution of facial
features, and in some cases, too many human objects in the detected
frames will be looking in a direction other than toward the
surveillance camera.
[0133] All foreground pixels are checked by the object detection
module 110. In some frames, there may be no identified objects in
the area under surveillance. In frames of interest, one or more
pixels will be identified having a probability greater than a
predetermined value corresponding to the location of a
predetermined portion of a detected object. A detected object will
generally occupy a substantial portion of a frame.
[0134] An original full image may have multiple scales that are
re-sized to different scales. The algorithm includes multiple
interlaced convolution layers and subsampling layers. Each "node"
in a convolution layer may have 5.times.5 convolutions. The
convolution layers have different number of sub-layers. Nodes
within each sub-layer have same configuration, that is, all nodes
have same convolution weights. The output is a probability map
representing the probabilities of human heads and/or upper torso
being located at a corresponding location at some scale. Those
probabilities either above a threshold amount or those certain
number of highest probabilities are selected as object
detections.
[0135] Bounding boxes are preferably drawn over the foreground
blobs identified as human object detections. These bounding boxes
are basically rectangles that are drawn around a selected position
of an object. Bounding boxes are generally used to specify location
and size of the enclosed object, and they preferably move with the
object in the video frames.
[0136] FIG. 9a shows an image captured by the video camera 102 that
corresponds to a single frame, and which may be digitized at module
104. Much of the detail captured within the frame includes
background 901, which may include static objects and interior items
and structure of the area under surveillance that appear in
substantially all frames and are not of interest to be tracked in
the surveillance algorithm. These "background" items are subtracted
pixel by pixel from the frame leaving foreground pixels or blobs
generated by adaptive background modeling. The background modeling
used in a system in accordance with a preferred embodiment is
"adaptive", such that it adapts to changes in lighting,
temperature, positions of background objects that may be moved,
etc.
[0137] Background modeling is illustratively used to identify the
image background. This procedure preferably involves an adaptive
background modeling module which deals with changing illuminations
and does not require objects to be constantly moving or still. Such
adaptive background module may be updated for each frame, over a
certain number of frames, or based on some other criteria such as a
threshold change in a background detection parameter. Preferably,
the updating of the background model depends on a learning rate
.rho., e.g.:
.mu..sub.t=(1-.rho.).mu..sub.t-1+.rho.X.sub.t; and
.sigma..sub.t.sup.2=(1-.rho.).sigma..sub.t-1.sup.2+.rho.(X.sub.t-.mu..sub.-
t).sup.T(X.sub.t-.mu..sub.t);
[0138] where .mu..sub.t, .sigma..sub.t are the mean and variation
of the Gaussian, and X.sub.t the pixel value at frame t,
respectively. Items that are well modeled are deemed to be
background to be subtracted. Those that are not well modeled are
deemed foreground objects and are not subtracted. If an object
remains as a foreground object for a substantial period of time, it
may eventually be deemed to be part of the background. It is also
preferred to analyze entire area under surveillances at a same time
by looking at all of the digitized pixels captured
simultaneously.
[0139] There are two walking human objects 902 and 904 in the image
captured and illustrated at FIG. 9a that are of interest in the
detection and tracking algorithm.
[0140] FIG. 9b illustrates a foreground blob 908 corresponding to
the two human objects 902 and 904 of the image of FIG. 9a and
results from the background subtraction process 106. This
foreground blob 908 is analyzed for human object detection.
[0141] The spots shown in FIG. 9c represent locations that have
sufficiently high probabilities of being human objects detected by
the convolutional neural network at 110, which have been refined
through optical flow projections 114 and undergo non-maximum
suppression. Each of the two spots 930 and 932 correspond to one of
the two human objects 902 and 904 which were detected. The two
spots 930 and 932 shown in FIG. 9c are determined to be situated at
particular locations within the frame that corresponds to a
predetermined part of the human object, such as the center of the
top of the head, or side or back of the head or face, or center of
upper torso, etc.
[0142] FIG. 9d shows the corresponding bounding boxes 934 and 936
overlaid in the original image over the upper torso and heads of
the two human objects 902 and 904. The bounding boxes 934 and 936
have been described above.
[0143] FIG. 9e demonstrates object trajectories 938 and 920
computed over multiple frames. The likely trajectories 938 and 920
illustrated at FIG. 9e show that the two human objects 902 and 904
came from the door zone 903 (see FIGS. 9a and 9d) at almost the
same time and are walking away from the door in the area under
surveillance. As described earlier, this may be a behavioral
circumstance where an alert code may be sent, e.g., if only one of
the two people swiped a card and either both people or the other
person of the two walked through the door from the non-secure area
on the other side.
Experiments
[0144] The system has been tested at an actual facility. On six
test videos taken at the facility, the system achieves 95.5%
precision in events classification. The violation detection rate is
97.1% and precision is 89.2%. The ratio between violations and
normal events is high because facility officers were asked to make
intentional violations. Table 1 lists some detailed results. The
system achieved overall 99.5% precision computed over one week's
data. The violation recall and precision are 80.0% and 70.6%,
respectively. Details are shown in Table 1 below.
2 videos events violations detected violations false alerts test
112 34 33 4 real out 1732 15 12 5
[0145] Table 1. Recall and precision of violation detection on 6
test videos and one week's real video.
[0146] An advantageous multiple object tracking algorithm and
surveillance system and methods based on which an alert reasoning
module is used to detect anomalies have been described. The
tracking system is preferably built on a graphical representation
to facilitate multiple hypotheses maintenance. Therefore, the
tracking system is very robust to local object detection results.
The pruning strategy based on image information makes the system
computation efficient.
[0147] The alert reasoning module takes advantage of the tracking
results. Predefined rules may be used to detect violations such as
piggy-backing and tailgating at access points. Human reviewers
and/or machine learning technologies may be used to achieve manual
and/or autonomous anomaly detection.
[0148] While an exemplary drawings and specific embodiments of the
present invention have been described and illustrated, it is to be
understood that that the scope of the present invention is not to
be limited to the particular embodiments discussed. Thus, the
embodiments shall be regarded as illustrative rather than
restrictive, and it should be understood that variations may be
made in those embodiments by workers skilled in the arts without
departing from the scope of the present invention as set forth in
the claims that follow and their structural and functional
equivalents. As but one of many variations, it should be understood
that systems having multiple "stereo" cameras or moving cameras may
benefit from including features of the detection and tracking
algorithm of the present invention.
[0149] In addition, in methods that may be performed according to
the claims below and/or preferred embodiments herein, the
operations have been described in selected typographical sequences.
However, the sequences have been selected and so ordered for
typographical convenience and are not intended to imply any
particular order for performing the operations, unless a particular
ordering is expressly provided or understood by those skilled in
the art as being necessary.
[0150] Co-Pending Patent Applications
[0151] The following list of United States patent applications,
which includes the application that matured into this patent, were
all filed on the same day and share a common disclosure:
[0152] I. "Video surveillance system with rule-based reasoning and
multiple-hypothesis scoring," Ser. No. ______;
[0153] II. "Video surveillance system that detects predefined
behaviors based on movement through zone patterns," Ser. No.
______;
[0154] III. "Video surveillance system in which trajectory
hypothesis spawning allows for trajectory splitting and/or
merging," Ser. No. ______;
[0155] IV. "Video surveillance system with trajectory hypothesis
spawning and local pruning," Ser. No. ______;
[0156] V. "Video surveillance system with trajectory hypothesis
scoring based on at least one non-spatial parameter," Ser. No.
______;
[0157] VI. "Video surveillance system with connection probability
computation that is a function of object size," Ser. No. ______;
and
[0158] VII "Video surveillance system with object detection and
probability scoring based on object class," Ser. No. ______;
* * * * *